ABRF 2018

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Speaker: Suvarna Gandlur, Associate Director, NGS Marketing, Takara Bio USA, Inc.

Advancements in next-generation sequencing (NGS) have made the technology widely available to scientists, researchers, and clinicians trying to elucidate unanswered questions in life sciences, understand disease mechanisms, and develop more effective methods for diagnosis and treatment. Improvements to methods for preparing nucleic acids for sequencing have been an essential part of these efforts, allowing even smaller, degraded, and complex samples to be investigated by sequencing. Our SMARTer NGS portfolio has contributed to these advancements by enabling the generation of NGS libraries from single cells, low-input, and other challenging sample types and providing streamlined workflows that are efficient, convenient, automation-friendly, and less prone to errors. In this talk, we presented recent developments to our SMARTer ThruPLEX portfolio, which uses a novel chemistry and adapter design to enable an addition-only, true single-tube protocol that is highly sensitive and free of any intermediate transfer, cleanup, or optimization steps. We introduced a new technology that minimizes ligation-induced bias in microRNA library preparation, allowing microRNAs to be captured and analyzed with high accuracy, sensitivity, and reproducibility.

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Liquid biopsies provide a noninvasive method to acquire the genetic information provided in cell-free DNA (cfDNA). Access to this genetic information through next-generation sequencing (NGS) identifies mutations and alterations that may play a role in cancer and other diseases. The key to identifying rare mutations is improved sequencing accuracy and the ability to distinguish between biological and PCR duplicates. SMARTer ThruPLEX Tag-seq was developed with the addition of unique molecular tags (UMTs) to improve sequencing accuracy by accounting for polymerase and sequencing errors and to increase confidence in rare allele identification. Libraries were prepared with SMARTer ThruPLEX Tag-seq using 10–30 ng of Horizon Discovery's Multiplex I cfDNA Reference Standard Set containing six single nucleotide variants (SNV) for four different genes (EGFR, KRAS, NRAS, PIK3CA) present at 0.5–5% allele frequency. The libraries were enriched with either a 110-kb or 240-kb custom panel or the Agilent ClearSeq Comprehensive Cancer Panel. Enriched libraries were sequenced with an average total read coverage of approximately 5,000X and analyzed with and without the UMTs. Deduplication without molecular tags reduced coverage to 295X; whereas, deduplication with UMTs allowed separation of biological duplicates from PCR duplicates and increased coverage to 2,110X, a significant reduction in false positives, 73% elimination of background noise, and a 10-fold increase in unique coverage compared to deduplication without UMTs. Employing UMT consensus reads, the sensitivity to detect 70 SNVs at 1% MAF was increased from 30% to 95% reads, and at 0.2% MAF increased from 7% to 75% and false positive calls reduced by 32X. Therefore, use of UMTs in the preparation of NGS libraries from cfDNA enhances sequencing accuracy: by distinguishing between biological duplicates and PCR duplicates, increasing read coverage and decreasing background noise, reducing false positives, and in more confident mutation calls.

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MicroRNA sequencing (miRNA-seq) is a useful tool for aiding researchers in the examination of miRNA expression patterns, the characterization of novel miRNAs, and for uncovering miRNA-disease associations. Since miRNAs are also unusually well-preserved in a range of specimens (e.g., urine, FFPE tissue, plasma), profiling their expression could become a powerful diagnostic tool. However, current methods for sequencing miRNA require large amounts of total RNA, are not very reproducible, and more importantly, have considerable systematic bias resulting in loss of many prospective biomarkers. This bias severely affects the trustworthiness of results as libraries are not a true representation of the biological state of the sample.

We have recently developed the SMARTer microRNA-Seq Kit that uses MAGIC (Mono-Adapter liGation and Intramolecular Circularization) technology to efficiently capture miRNA species with extremely low bias. Libraries prepared using an equimolar mixture of 963 miRNAs, sequenced on Illumina platforms and analyzed for read distribution reveal that >70% of miRNAs captured fall within a +/– 2-fold variation of the expected read number they should receive. This means that the expression level of ~70% of miRNAs in the equimolar mixture was truly and accurately represented. In contrast, frequency distribution analyses for kits from competitors N, I, and B revealed that 49–79% of miRNAs are greatly under-represented (i.e., less than 2X fewer reads than expected), 13–35% of miRNAs are represented within a +/– 2-fold variation of the expected read number, and around 8–14% are over-represented by more than 2X. These findings highlight the importance of understanding the current technical state of miRNA sequencing technologies to better prepare for analyzing and validating miRNA expression data. Additionally, the SMARTer microRNA-Seq Kit is designed and developed to more accurately reflect the true biological state of a sample, which will be an important factor as miRNA research moves toward diagnostic tools specific for personalized medicine.

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Since the emergence of next-generation sequencing (NGS), the importance and demand for single-cell analysis have risen rapidly. Single-cell RNA-seq generates data for various analysis methods such as differential gene expression, alternative splicing, gene fusion identification, and so on, which allow for cell profiling in great detail. As a result, single-cell RNA-seq has been gaining prominence not only in basic research fields but also in clinical fields. Extracting meaningful biological information from the small amount of mRNA present in a single cell requires a library preparation method with exceptional sensitivity and reproducibility. By providing the capability to obtain full-length mRNA sequence information (as opposed to merely capturing transcript 3' ends), the SMART‑Seq v4 Ultra Low Input RNA Kit for Sequencing (SMART‑Seq v4) offers the most advanced single-cell RNA-seq method on the market. However, this method is relatively low-throughput, while researchers are interested in analyzing hundreds or thousands of individual cells for any given experiment. To address this need, our single-cell RNA-seq technology was further modified to create a simplified, high-throughput workflow with very little hands-on time. The reverse transcription (RT) and PCR amplification steps were combined into a single RT-PCR step so that users can simply set up the RT-PCR and walk away. The updated workflow, available in the SMART‑Seq HT Kit, is extremely fast, convenient, and generates a higher cDNA yield than its predecessor, all while providing the same unparalleled sensitivity and reproducibility.

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Since the emergence of next-generation sequencing (NGS), the importance and demand for single-cell analysis have risen rapidly. Single-cell RNA-seq generates data for various analysis methods such as differential gene expression, alternative splicing, gene fusion identification, and so on, which allow for cell profiling in great detail. As a result, single-cell RNA-seq has been gaining prominence not only in basic research fields but also in clinical fields. Extracting meaningful biological information from the small amount of mRNA present in each cell requires a library preparation method with exceptional sensitivity and reproducibility. The SMART‑Seq v4 Ultra Low Input RNA Kit for Sequencing offers the most advanced single-cell RNA-seq method on the market, in part due to its incomparable capability to retrieve information from full-length mRNA and not just the 3' end. However, this method can only capture polyadenylated mRNA, and thus works best with high-quality RNA and cells. In addition, it does not preserve strand-of-origin information. To address these problems, we have further modified our SMART RNA-seq technology to create the SMART‑Seq Stranded Kit, a single-cell-capable workflow that relies on random priming instead of oligo(dT) priming, hence capturing any RNA regardless of polyadenylation status. The SMART‑Seq Stranded Kit preserves the strand-of-origin information, making it more amenable to distinguishing overlapping genes and for comprehensively annotating and quantifying lncRNAs. The SMART‑Seq Stranded Kit offers a workflow that delivers a more accurate representation of the single-cell transcriptome than is achievable with current methods.

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Genomics research requires a significant sample size to provide robust biological signal, leaving researchers clamoring for access to large sample sets. As this research continues to expand into the clinical arena, the demand to sequence RNA from banked tissue samples, such as formalin-fixed paraffin-embedded blocks, is unavoidable. Recovering DNA and RNA from such samples can be challenging depending on age of the sample block and fixture protocols. To fulfill the need for increased recovery of usable reads, several manufacturers (including Takara Bio) have developed solutions to address these challenges, including FFPE-specific extraction kits, as well as library synthesis and quality control reagents. In this study, the authors (Dragon et al.) analyzed the quality and outcome of RNA-seq data generated from three library synthesis kits of FFPE-derived human thyroid tumors with storage times from 3–6 years.

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The ABRF Genomics Research Group (GRG) generated data from hundreds of individual SUM149PT cells treated with the histone deacetylase inhibitor TSA vs. untreated controls across several scRNA-Seq platforms (Fluidigm C1, Takara Bio SMARTer ICELL8 system, 10X Genomics Chromium Controller, and Illumina/BioRad ddSEQ). The goals of this project were to demonstrate RNA sequencing (RNA-Seq) methods for profiling the ultra-low amounts of RNA present in individual cells, and RNA amplification using the various currently available platforms. The authors (Chittur et al.) discussed the results of the study as well as technical challenges/lessons learned and presented general guidelines for best practices in sample preparation and analysis.